Question.5475 - Locate two peer-reviewed article (related to the ethics) published within the last 3 years in a peer-reviewed journal from the online library, then present an assessment comprising of a precise and critical evaluation. Do not summarize the article. First synthesize the position of the authors. Then, evaluate the information presented in the article. Evaluate the information presented in the article for truth. Is there bias or faulty reasoning? You must support claims of both fact and faulty reasoning. What are both author's saying the same topic? Do they agree or is there conflicting information. Draw conclusions.
Answer Below:
Ethics in Supply Chain ManagementBUSN 501Ethics in Supply Chain ManagementThe integration of ethical considerations into supply chain management has b...
Ethics xx Supply xxxxx ManagementBUSN xxxxxx in xxxxxx Chain xxxxxxxxxxxxx integration xx ethical xxxxxxxxxxxxxx into xxxxxx chain xxxxxxxxxx has xxxxxx a xxxxx topic xx both xxxxxxxx and xxxxxxxxxxxxxxxxx circles xxxxxx et xx and xxxx and xxxx examine xxxxxxxxxx ethics xxx organizational xxxxxxxx from xxxxxxxxx angles xxxxxx et xx dissect xxx promises xxx perils xx artificial xxxxxxxxxxxx in xxxxxxxxx and xxxxxx chain xxxxxxxxxx while xxxx and xxxx illuminate xxx ethical xxxxxxxxxx drives xxxxx supply xxxxx integration xxxx studies xxxxxxxxx the xxxxxxx nature xx ethical xxxxxx chain xxxxxxxxxx yet xxxx diverge xx conceptual xxxxxxxxxx methodological xxxxx and xxxxxxxxx implications x critical xxxxxxxxx reveals xxxxxxxx between xxxxxxxxxxxxx determinism xxx human xxxxxx while xxxx exposing xxxxxxxx inequities xxxxxxxx in xxxxxx networks xxxxxx et xx position xxxxxxxxxx AI xx a xxxxxxx shift xx operational xxxxxxxxx p xxxx review xx applications xxxx as xxxxxxx language xxxxxxxxxx robotic xxxxxxx automation xxx computer xxxxxx in xxxxxxxxxxx and xxxxxxxxxxxx They xxxx that xxxxxx algorithms xxxx perpetuating xxxxxx in xxxxxxxx selection xxx that xxxx hallucinations xxxxxxxx supply xxxxx resilience xxxx argue xxxx AI x constrained xxxxxxx to xxxxxxxxxx analyze xxx benefits xxx drawbacks xx integrating xxxxxxxx business xxxxxx p xxxxxxx human xxxxxxxxx They xxxxxxx note xxxx million xxxxxxxxx positions xxxxxxxx face xxxxxxxxx replacement xx AI x highlighting xxx tension xxxxxxx efficiency xxxxx and xxxxxxxxx displacement xxxxx five-level xxxxxxxx framework xxxxx factors xxxxxxxxxxx AI xxxxxxxx through xx theoretical xxxxxxxxxxxxxxxxxxx but xx relies xxxxxxx on xxxxxxxxxxx futures xxx conceptual xxxxxxxxx with xxxxxxx empirical xxxxxxxxxx In xxxxxxxx Wang xxx Feng xxxxxx their xxxxxxxx in xxxxxxxxxxxx organizational xxxxxxxxx drawn xxxx Chinese xxxxxxxxxxxxx firms xxxx propose xxx construct xx supply xxxxx ethical xxxxxxxxxx SCEL xxx test xxx impact xx green xxxxxx chain xxxxxxxxxxx through x moderated xxxxxxxxx model xxxxx findings xxxx that xxxxxxx leadership xxxxxxx by xxxxxxx who xxxxxxxxxxx integrity xxx set xxxx ethical xxxxxxxxx directly xxxxxxxx strategic xxxxxxxxxxx by xxxxxxxxxxx a xxxxxx green xxxxx They xxxxxxx demonstrate xxxx perceived xxxxxxxxxxxxx pressures xxxxxxxxxx SCEL x effects xx amplifying xxxxxx learning xxxxxxxxxx thereby xxxxxxxxxxx the xxxxx firm x credibility xxxxx hierarchical xxxxxxxxxx analysis xxxxxxxx robust xxxxxxxx and xxxxxxxxx validation xx test xxxxxxxxx by xxx green xxxxx and xxxxxxxxxx by xxxx business xxx social xxxxxx Despite xxxxx strengths xxxx rely xx self-reported xxxxxx data xxxx a xxxxxx national xxxxxxx raising xxxxxxxx about xxxxxx desirability xxxx common xxxxxx variance xxx limited xxxxxxxx validity xxxxxxxxxxxxxxxx these xxxxx show xxxxxxxxxxx commitments xxx different xxxxxxxxxx Richey xx al xxxxxxx a xxxxxxxxxx framework xxx they xxxxxxxxxx existing xxxxxxxxxx They xxxxxxx potential xxxxxxxxxxxx for xx in xxxxxx chain xxxxxxxxxx and xxxx sometimes xxxxxxxx untested xxxxxx with xxxxxxxxxx projections xxxx do xxx provide xxxxxxxxxx data xxx they xxxx Goldman xxxxx projections xx job xxxxxxxxxxxx without xxxxxxxxxx AI-driven xxx creation xxxxx framework xxxxxxx scholars xx explore xxxxxxxxx solutions xxx they xxxxxxx liquid xxxxxx networks xx improve xxxxxxxxxxxx They xxxx clear xxxxxxxxxxx definitions xxx they xx not xxxxxxx data-driven xxxxxxxxxx for xxxxxxxxxx Wang xxx Feng xxx quantitative xxxxxxx and xxxx apply xxxxxxxxxxxx regression xxx moderated xxxxxxxxx analysis xx test xxxxxxxxxx They xxxxxxx supply xxxxx ethical xxxxxxxxxx and xxxx assess xxxxx image xxx institutional xxxxxx with xxxxxxxxx scales xxxx also xxxxxxxx integration xxxxxxxx but xxx homogeneity xx their xxxxxx limits xxxxxxxxxxxxxxxx They xxxx on xxxxxxxxxxxxxxx data xxx this xxxxxxx external xxxxxxxx Despite xxxxx differences xxxxxxxx convergences xxx present xxx both xxxxxxxx emphasize xxxxxxxxxxx trust xx crucial xx ethical xxxxxx chain xxxxxxxxxx Richey xx al xxxxxxx that xx s xxxxxx methodology xxxxx erode xxxxxxx confidence xxxx and xxxx demonstrate xxxx the xxxxx image xxxxxxxx supply xxxxx ethical xxxxxxxxxx s xxxxxx and xxxx note xx transmits xxxxxxxx signals xx credibility xxxx groups xxxxxxxx institutional xxxxxxxxx as xxxxxxxxxxx of xxxxxxx practices xxx these xxxxxxxxx include xxxxxxxxxx mandates xxx consumer xxxxxxxxxxxx They xxxxx that xxxxxxxxxxx communication xxxxxxxx with xxxxxxxxxxxxx alignment xxxxx partners xxxxxxx favorable xxxxxxxxxx for xxxxxxxxxxxxx innovation xxx ethical xxxxxxxxxxx Their xxxxxxxxxxxxx diverge xxx each xxxxxxx prioritizes xxxxxxxxx solutions xxxxxx et xx advocate xxxxxxxxx solutions xxx they xxxxxxxxx liquid xxxxxx networks xx improve xxxxxxxxxxx transparency xxxx recommend xxxxxx AI xxxxxxxxxxxx logs xxx support xxxxxxxxxxxx AI xxxxxxx for xxxx and xxxxxxxxxxxxx elimination xxxx and xxxx emphasize xx human xxxxxxxxxxxxx and xxxx talk xx training xxxxxxx This xxxxxxxx leadership xxxxxxxxxxx workshops xx order xx propose xxxxxxx behaviors xxx to xxxxxx the xxxxx behaviors xx the xxxxxx chain xxxxxx et xx call xxx algorithmic xxxxxxxxxxxx and xxxx and xxxx demand xxxxxxxx transparency xxxxx differences xxx in xxxxxx to xxxxxx mechanisms xxx each xxxxxxx addresses xxxxxx from x different xxxxxxxxxxx Richey xx al xxxxxx that xxxxxxx results xxxx be xxxxxxxx when xxxxxxxx AI xxxxxxxxxxx are xxxx and xxx shielded xxx first xxxxxxxxxx that xxxx and xxxx have xxxx is xxxx ethical xxxxxx starts xxxx ethically xxxxxxxxx leaders xxx disseminate xxx right xxxxxx through xxxxxxxx the xxxxxxx social xxxxxxxx It xxxxxxxx supports xxxx both xx them xxx strengths xxx none xx them xxx fully xxxxxxx the xxxxxx processes xxxxxx to xxxx and xxxxxxx The xxxxxxxx have xxxxx gaps xxxx it xxxxx to xxxxx dynamics xxx structural xxxxxxxxxxxx Richey xx al xxxxxxx mention xxxx AI xxxxx unjustly xxxxxxxxxx certain xxxxxxxxx and xxxx do xxx explore xxxxxxxxxxx governance x potential xx concentrate xxxxx in xxxxxxxxxxxxx corporations xxxxxxx partners xx the xxxxxx South xxxxx be xxxxxxxxxxxx and xxxx issue xxxxxxx unaddressed xxxx and xxxx s xxxxx assumes xxxx ethical xxxxxxxxxx will xxxxxxx downstream xxxxxxx resistance xxxx overlook xxx possibility xxxx ethical xxxxxxxx might xxxxxxxx dissent xx obscure xxxxxxxxxxxx practices xxxxxx a xxxxx veneer xxxxxxx article xxxxxxx with xxxxxxxxxxxx critiques xxxx examine xxx defines xxxxxxx norms xx global xxxxxx chains xxxxxxx weakness xx the xxxxxxxxxxx from xxxxxxxx realities xxx both xxxxxxx underplay xxxxxxxx factors xxxxxx et xx discuss xxxxxxxx and xxxxxxxxxx transformations xx broad xxxxx and xxxx underplay xxxxx conditions xxx environmental xxxxxxxxxxxxx Wang xxx Feng xxxxx on xxxxxxxxxxx sharing xxx joint xxxxxxxxxxxxxxx and xxxx overlook xxxxxxxx dependencies xxx contractual xxxxxxxxxxx They xxxxxx geopolitical xxxxxxxxx that xxxxx partners xxxxxxx to xxxxxxxxx green xxxxxxxxx Both xxxxxxx would xxxxxxx from x critical xxxxxxx approach xxxx situates xxxxxxxxxxxxx and xxxxxxxxxx interventions xxxxxx broader xxxxxxxx and xxxxxxxxx contexts xxxxxx research xxxxxx bridge xxxxxxxxxx AI xxxxxxxxxx with xxxxxxxxx leadership xxxxxxx and xxxxxxxxx both xxxxxxxxxxxx Mixed-methods xxxxxxx could xxxxxxx algorithmic xxxxxx with xxxxxxxxxxxx fieldwork xxxxx supply xxxxx participants xxxxxxxxxxx should xxx how xx tools xxx leadership xxxxxxxxx co-evolve xx uneven xxxxxxxxxxxx economies xxxxxxxxxxx should xxxx how xxxxxxxxxxx in xxxx and xxxxxxx interact xxxx institutional xxxxxxxxxx that xxxxxxx regulatory xxxxxxx and xxxxx unions xxxxxxxxxxx should xxxxxxxx community-based xxxxxxxxx mechanisms xxxx integrative xxxxxxxxxxx could xxxxxxx cross-functional xxxxx and xxxxx teams xxxxx include xxxx scientists xxx ethicists xxxxxx chain xxxxxxxx and xxxxxxxxx workers xxxxx participate xx creating xxxxxxxxxxxx tools xxxx explain xxxxxxxxx supplier xxxxxxxx These xxxxxxxxxxxx could xxxxxxxxxx leadership xxxxxxxxx that xxxxxxx supplier xxxxxxxxxx and xxxx could xxx scenario xxxxxxxxx that xxxxxxx AI xxxxxxxxxxx with xxxxxxx deliberations xxxxx efforts xxxxx ensure xxxx technology xxx human xxxxxxxx inform xxxx other xxxxx pilot xxxxxxxx would xxxxxxxx rich xxxx and xxxx academics xxx practitioners xxxxx benefit xxxx the xxxxxxxx Case xxxxxxx would xxxxxx how xx recommendations xxx ethical xxxxx shape xxxxxx chain xxxxxxxxx in xxxxxxxx Power xxxxxxxx would xx uncovered xxxx algorithmic xxxxxxxxxxxx meets xxxxxxxxxxxxxx hierarchies xxxxx findings xxxxx illustrate xxxx to xxxxxxxxxxxx agency xxxxxx network xxxxxxxxxxxx and xxxxx insights xxxxx help xxxxxxxx unintended xxxxxxxxxxxx Policy xxxxxxxxxxxx arise xxxx the xxxx for xxxxxxxxxx approaches xxx companies xxxxxx act xx these xxxxxxxx Firms xxxxxx invest xx algorithmic xxxxxxxxxx structures xxx leadership xxxxxxxxxxx programs xxxx emphasize xxxxxxxxxxxxxx They xxxxxx design xx dashboards xxxx are xxxxxxxxx and xxxx should xxxxxxxxx cross-organizational xxxxxxx dialogues xxxx should xxxxx impact xxxxxxxxxxx that xxxxxxxxxxx data-driven xxxxxxxx and xxxxxxxx from xxxxxxx and xxxxxxxxx stakeholders xxxxxxxx these xxxxxxx together xxx improve xxxx theory xxx practice x research xxxxx on xxxxxxx constituents xxx critical xxxxxxx would xxxx light xx how xxxxxxxxxx and xx are xxxxxxxx developed xx GSCs xxxx would xxxxxx frameworks xxxx develop xxxxxx and xxxxxxxxxxxx architectures xxxxx meet xxxxx conditions xxxx synthesis xxxxxxxx a xxx out xx one x individual xxxxxxxxxxxxxx and xx the xxxx time xxxxxxx how xxx can xxxxx from xxx other xxxxxx et xx s xxxxxxxxxxxxxx vision xxx Wang xxx Feng x leadership xxxxx do xxx have xx be xxxxxxxx exclusive xxxxxxx what xxx emerged xx an xxxxxxxxxx approach xxxxx steer xxxxxx chain xxxx acknowledges xxxxxxxxxxxxx of xxxx code xxx culture xx the xxxxxxx It xxxxx stress xxxx the xxxxxxxxx security xxxxxxxx and xxx ethical xxxx models xxxx to xx mutually xxxxxxxxxxx in xxxxx to xxxxx intricate xxxxxxx Thus xx can xx concluded xxxx ethical xxxxxx chain xxxxxxxxxx is xx the xxxxxxxxxx and xxxxxxx changes xxx needed xx has xxxxxxxx from xxx areas xx artificial xxxxxxxxxxxx and xxxxxxxxxx and xx an xxxx that xxx not xxx been xxxx to xxxx a xxxxxxxxxxx synthesis xx code xxx culture xxxxxxxx should xxxxxxx dialectical xxxxxxxxxxxx in xxx future xxx both xxxxxxxxx and xxxxxxxxxxxx in xxx advancing xxxxx It xx also xxxxxxxxx for xxxxxx scholarship xx employ xxxxxxxx power xxx materialist xxxxx analysis xx this xxx the xxxxxxxxx is xxxx to xxxxxxx theories xxx reveal xxxxxxxxx instruments xx construct xxxxxx chain xx equality xxx more xxxxxxxxxxx ReferencesRichey x G xxxxxxxxx S xxxxx Sramek x Mihalis xxxxxxxxx Dwivedi x K xxxxxxxxxx intelligence xx logistics xxx supply xxxxx management x primer xxx roadmap xxx research xxxxxxx of xxxxxxxx Logistics xxxxx doi xxx jbl xxxx J xxxx T xxxxxx chain xxxxxxx leadership xxx green xxxxxx chain xxxxxxxxxxx a xxxxxxxxx mediation xxxxxxxx International xxxxxxx of xxxxxxxxx Research xxx Applications xMore Articles From Management
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